Q21: What is the cause of internet rush hour? Wiki
Short Answer Motivation and Overview You come home after a long day of work, sit down at your computer, and a popular youtube video shows up on your home page. You click on the video out of curiosity, and it begins to load. The first 10 seconds stream perfectly, and then the video needs to buffer and it continues to buffer, buffer, and buffer. This happens intermittently every 10 seconds or so until you become frustrated and try another video only to have the same problem. You try another popular website, and it’s the same story. So what’s really going on, and why can’t you just enjoy a couple of videos after a long day at work? The obvious reason is that the rest of the world is also home from work, and also trying to access the same popular videos; sharing scare resources among a pool of data hungry users inevitably leads to some waiting in line. A similar story could be said for customers at a bank. One person may go to the bank on their lunch break in hopes of making a quick deposit only to find out that many other people also went to the bank on their lunch break. Now a line has formed and almost everyone is waiting to be served. There are difference performance metrics for networks such as throughput and delay. Throughput is measured by the data rate (bps) and can be significantly reduced by protocol overheads. Delay takes many forms such as transmission delay (time for a transmitter to send all bits of a packet), propagation delay (time for one bit to transmit from source to destination), and processing delay (time required to process packets at the source, routers or destination). There is another form of delay known as queuing delay that is the subject of this Wiki article. Queuing delays are a significant component in the performance of communication networks. In a network where resources are shared (almost all communication networks we use today) performance generally deteriorates as demand moves toward capacity. Why do we model queueing delay? Congestion arises when the number of packets being transferred through a network exceeds the packet handling capacity of the network. A queue will grow as long as the arrival rate continues to exceed the transmission rate. Network delay increases as a queue grows larger, and because queues have finite capacity packets can be dropped, which is the worst case scenario for everyone. When a packet arrives at a router, it is stored in an input buffer where it waits in line to be served. Being served typically involves a routing decision being made. Once a packet has been served and a decision has been made as to where to send it, the packet moves to an appropriate output buffer where it waits in line to be transmitted. Two general scenarios can therefore cause congestion and queuing delay: when packets arrive to fast to be routed and the input buffers begin to fill; or if the packets arrive faster than can be transmitted, and the output buffers begin to fill. During peak internet hours some nodes in a network may reach capacity, causing packets to get discarded. This in turn leads to rerouting and congestion control messaging within the network which induces overhead. The extra overhead causes more buffers to overflow, and the retransmission of discarded packets caused heavier traffic. As delays continue to increase, even successfully delivered packets are retransmitted due to timeout. Therefore, queueing delay can have a significant snowball effect. There are different ways to reduce congestion. For instance a source can detect forms of congestion such as delay and then adjust its transmission flow as necessary. Networks use many flow control mechanisms to control congestion caused by queuing delay. One example of flow control could be hop by hop: If one node becomes congested, it could send a message to other neighboring nodes asking them to throttle there flow of packets; those neighbors would begin to induce larger queues, and in turn would signal to their neighbors to send less packets. This behavior propogates back to the source transmitting the packets, and is known as Backpressure. It is used in connection oriented networks that allow it. A system model Insert picture of queuing system here Kendall's Notation A/S/''c''/''K''/''N''/D Little's Theorem The average number of customers (N) is given by the following equation: N=λT Lambda is the average customer arrival rate and T is the average service time for a customer. Consider the example of a convenient store where the customer arrival rate (lambda) doubles but the customers still spend the same amount of time (T) in the store. This will double the number of customers in the convenient store. Similarly, if the customer arrival rate remains the same but the customer service time (like the time waiting to cash out) doubles this will also double the number of customers in the store. References (1) Alberto Leon-Garcia, “Probability, Statistics, and Random Processes for Electrical Engineering” Latest activity Photos and videos are a great way to add visuals to your wiki. Find videos about your topic by exploring Wikia's Video Library. Category:Browse